class_docstr: The Speech Commands dataset for keyword detection.
code: def _split_archive(self, train_archive):
      train_paths = []
      for path, file_obj in train_archive:
          if "testing_list.txt" in path:
              train_test_paths = file_obj.read().strip().splitlines()
              train_test_paths = [
                  p.decode("ascii") for p in train_test_paths
              ]
          elif "validation_list.txt" in path:
              validation_paths = file_obj.read().strip().splitlines()
              validation_paths = [
                  p.decode("ascii") for p in validation_paths
              ]
          elif path.endswith(".wav"):
              train_paths.append(path)
  
      # Original validation files did include silence - we add them manually here
      validation_paths.append(
          os.path.join(BACKGROUND_NOISE, "running_tap.wav")
      )
  
      # The paths for the train set is just whichever paths that do not exist in
      # either the test or validation splits.
      train_paths = (
          set(train_paths)
          - set(validation_paths)
          - set(train_test_paths)
      )
  
      return train_paths, validation_paths
  
file_path: datasets/tensorflow_datasets/datasets/speech_commands/speech_commands_dataset_builder.py
imports: import os
  import numpy
  from tensorflow_datasets.core import lazy_imports_lib
  import tensorflow_datasets.public_api
label_desc: Use image node_id 4 for calling the Builder obj's underlying member method code with example usage: obj._split_archive(train_archive) and returns: train_paths,  validation_paths